New energy vehicle thermal management control method

Abstract

The invention relates to the technical field of vehicle-mounted equipment control, in particular to a new energy vehicle heat management control method. The method comprises the following steps: collecting temperature data through a sensor, and carrying out preprocessing operation on the collected temperature data; setting an evaporator temperature protection threshold value based on the preprocessed temperature data, and constructing a multivariable adaptive control function; dynamically adjusting the rotating speed of a compressor, the state of a refrigerant electromagnetic valve and the closing time of the refrigerant electromagnetic valve through a model prediction control algorithm based on the constructed multivariable self-adaptive control function; and on the basis of the state of the refrigerant electromagnetic valve, the abnormal condition of accumulated refrigerant pressure under the single-time power-on condition is judged, and fault diagnosis and early warning of the refrigerant system are achieved. The cooling capacity distribution is dynamically adjusted through the obtained vehicle state, the system state and the environment information, and the purpose of cooling more effectively through the electric compressor under the condition that less energy is consumed is achieved.

Description

Technical Field

[0001] This invention relates to the field of vehicle-mounted equipment control technology, and more specifically, to a thermal management control method for new energy vehicles. Background Technology

[0002] With the rapid development of the new energy vehicle industry, thermal management systems, as an important component related to vehicle performance and comfort, are also constantly innovating and evolving. The thermal management systems of new energy vehicles have gradually evolved from traditional single-function designs to integrated designs. This integrated design is driven by the characteristics of new energy vehicles and their thermal management requirements.

[0003] New energy vehicles with integrated thermal management suffer from several drawbacks. The use of large-displacement electric air conditioning compressors leads to incomplete matching of the refrigeration system in the cab, causing the evaporator to frequently drop to its protection threshold. This results in frequent compressor start-stop cycles, significantly shortening the compressor's lifespan. Furthermore, the cab temperature is maintained solely by the compressor's frequent start-stop cycles, wasting excess battery energy that cannot be effectively converted into cooling for other components. Additionally, pre-cooling the battery via water circuits before the compressor needs to be activated slows down the battery's temperature rise, further reducing overall vehicle energy consumption. Therefore, this paper proposes a thermal management control method for new energy vehicles. Summary of the Invention

[0004] This invention proposes a novel thermal management control method for new energy vehicles. It aims to address the problems in existing new energy vehicles with integrated thermal management systems. Due to the use of large-displacement electric air conditioning compressors, when the cab is cooled alone, incomplete matching of the refrigeration system causes the evaporator to frequently drop to the protection threshold, leading to frequent compressor start-stop cycles and significantly shortening the lifespan of the electric compressor. Furthermore, the cab temperature is maintained solely by the frequent compressor start-stop cycles, and the excess battery energy wasted cannot be effectively converted into cooling for other components. The proposed method addresses this issue by pre-cooling the battery via a water circuit before the compressor needs to be activated, slowing down the battery's temperature rise and further reducing overall vehicle energy consumption.

[0005] To achieve the above objectives, the present invention aims to provide a thermal management control method for new energy vehicles, comprising the following steps:

[0006] S1. Collect temperature data through sensors and perform preprocessing operations on the collected temperature data;

[0007] S2. Set the evaporator temperature protection threshold based on the preprocessed temperature data and construct a multivariable adaptive control function;

[0008] S3. Based on the constructed multivariable adaptive control function, the compressor speed, the state of the refrigerant solenoid valve, and the closing time of the refrigerant solenoid valve are dynamically adjusted through the model predictive control algorithm.

[0009] S4. Based on the status of the refrigerant solenoid valve, the system judges the cumulative refrigerant pressure abnormality under a single power-on condition to realize refrigerant system fault diagnosis and early warning.

[0010] As a further improvement to this technical solution, in step S1, the temperature data includes the cab temperature, evaporator temperature, vehicle ambient temperature, and ambient solar radiation intensity data.

[0011] As a further improvement to this technical solution, in step S1, temperature data is collected by a sensor, and the collected temperature data is preprocessed. The specific steps of the preprocessing operation are as follows:

[0012] S1.1 Identify and remove outliers using the standard deviation in statistical methods, and fill in missing values ​​using linear interpolation in interpolation methods;

[0013] S1.2, Based on the temperature data in S1.1, the Butterworth filter technique in the low-pass filtering technology is used to reduce the impact of noise on temperature measurement;

[0014] S1.3. The temperature data after filtering is normalized using Z-Score to convert data of different scales to the same range.

[0015] As a further improvement to this technical solution, in step S2, an evaporator temperature protection threshold is set based on the pre-processed temperature data. When the evaporator temperature approaches the evaporator temperature protection threshold, the system will adjust the refrigerant flow direction by opening the refrigerant solenoid valve on the BMS side to prevent the evaporator from becoming too cold.

[0016] As a further improvement to this technical solution, the specific steps in S2 for constructing a multivariable adaptive control function based on the preprocessed temperature data are as follows:

[0017] S2.1 Define the state vector x based on the cab temperature and evaporator temperature, and define the disturbance input vector d based on the vehicle's external ambient temperature and external solar radiation intensity;

[0018] S2.2 Define the control input vector u according to the vehicle's heating power;

[0019] S2.3 Construct a multivariable adaptive control function f(x,u,d) based on the state vector x obtained in S2.1, the disturbance input vector d, and the control input vector u obtained in S2.2.

[0020] As a further improvement to this technical solution, in step S2.3, the rate of change of the state vector x is derived based on the state vector x, the disturbance input vector d, and the control input vector u, thereby constructing a multivariable adaptive control function f(x,u,d).

[0021] As a further improvement to this technical solution, in step S3, the specific steps for dynamically adjusting the compressor speed, the state of the refrigerant solenoid valve, and the closing time of the refrigerant solenoid valve based on the constructed multivariable adaptive control function and the model predictive control algorithm are as follows:

[0022] S3.1, Extend the compressor speed and refrigerant solenoid valve status into the control input vector u;

[0023] S3.2. Define the objective function J based on the control input vector u. The objective function J is then expressed as:

[0024]

[0025] In the formula, N represents the prediction time domain length; T cabin (k+i) represents the cab temperature at time k+i; T target Indicates the target temperature of the cab; Q heater (k+i) represents the heating power at time k+i; u compressor (k+i) represents the compressor speed at time k+i; u valve (k+i) represents the state of the refrigerant solenoid valve at time k+i; t off,ref w1 indicates the desired refrigerant solenoid valve closing time; w2 indicates the importance of the rate of change in cab temperature error; w3 indicates the importance of the rate of change in heating power consumption; w4 indicates the importance of the rate of change in compressor speed; w5 indicates the importance of the rate of change in refrigerant solenoid valve status; w5 indicates the importance of the deviation of the refrigerant solenoid valve closing time from the reference value.

[0026] S3.3. The control input sequence u(k) at each sampling time k is obtained by solving for the minimum value of the objective function J, and the objective function J is constrained according to the state update equation and the upper and lower limits of the control input vector u.

[0027] S3.4. Based on the first control action u(k), dynamically adjust the compressor speed, the state of the refrigerant solenoid valve, and the closing time of the refrigerant solenoid valve, and recalculate the optimal control sequence at the next sampling time.

[0028] As a further improvement to this technical solution, in step S3.2, the weight coefficients of each term in the objective function J are adjusted using an adaptive weight adjustment technique, wherein the adjustment method for each weight coefficient is as follows:

[0029] S3.2.1 Calculate the deviation and rate of change of the weight coefficients of the objective function J based on the deviation of the performance index;

[0030] S3.2.2 Based on the deviation and rate of change obtained in S3.2.1, the weight coefficients are dynamically adjusted using the PID adjustment method, and normalization is performed after each adjustment.

[0031] S3.2.3. Set the trigger conditions for weight adjustment based on the adjusted weight coefficients to reduce computational overhead;

[0032] S3.2.4 Repeat steps S3.2.1 to S3.2.3 at each sampling time until the system performance meets the design requirements.

[0033] As a further improvement to this technical solution, in step S3.3, based on the constraint objective function J of the state update equation, the expression of the state update equation is:

[0034] The state update equation for adjusting the compressor speed is:

[0035] u compressor (k+1)=a2(T target -T evaporator (k))+b2u compressor (k);

[0036] In the formula, u compressor (k+1) represents the compressor speed at time k+1; a2 is used to convert the temperature difference into a change in compressor speed; b2 determines the degree of influence of the current compressor speed on the speed at the next time step.

[0037] The state update equation for adjusting the refrigerant solenoid valve state is:

[0038] u valve (k+1)=u valve (k)+f(u valve (k));

[0039] In the formula, u valve (k+1) represents the state of the refrigerant solenoid valve at time k+1;

[0040] The state update equation for adjusting the closing time of the refrigerant solenoid valve is:

[0041] t off (k+1)=(1-u valve (k))·(t off (k)+Δt);

[0042] In the formula, t off (k+1) represents the closing time of the refrigerant solenoid valve at time k+1.

[0043] As a further improvement to this technical solution, in S3.3, based on the objective function J for the upper and lower limit constraints of the control input vector u, the expression for the upper and lower limit constraints of the control input vector u is:

[0044] The heating power constraint is:

[0045]

[0046] Where, Indicates the minimum allowable heating power of the system; Indicates the maximum allowable heating power of the system;

[0047] The compressor speed constraint is:

[0048]

[0049] Where, Indicates the minimum allowable speed of the compressor; Indicates the maximum allowable speed of the compressor;

[0050] The refrigerant solenoid valve state constraints are as follows:

[0051] u valve ∈{0,1};

[0052] In the formula, u valve =0 indicates that the refrigerant solenoid valve is closed; u valve =1 indicates that the refrigerant solenoid valve is open;

[0053] The closing time constraint for the refrigerant solenoid valve is:

[0054]

[0055] Where, Indicates the minimum allowed shutdown time; Indicates the maximum allowed shutdown time.

[0056] As a further improvement to this technical solution, in S4, based on the state of the refrigerant solenoid valve, the cumulative refrigerant pressure abnormality under a single power-on condition is judged to realize refrigerant system fault diagnosis and early warning. When the frequent start-stop caused by the refrigerant pressure abnormality exceeds 5 times, the compressor cannot be started again under that power-on condition.

[0057] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0058] By dynamically adjusting the cooling capacity distribution based on acquired vehicle status, system status, and environmental information, the system achieves cooling more effectively through the electric compressor while consuming less energy. This ensures the battery operates within its optimal temperature range, thereby improving charging and discharging efficiency and extending battery life. A precise thermal management system effectively allocates cooling or heating resources, reducing unnecessary energy consumption and improving the overall system's energy efficiency. Power batteries are highly sensitive to temperature, and under extreme conditions, thermal runaway and other safety hazards may occur. A good thermal management system can prevent overheating, ensuring the safety of passengers and the vehicle. Effective thermal management helps to quickly regulate the temperature inside the driver's cabin, providing a comfortable environment for the driver and passengers. During fast charging, the battery generates a significant amount of heat. Attached Figure Description

[0059] Figure 1 This is a flowchart of the overall method of the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Example: See Figure 1 As shown, this embodiment provides a thermal management control method for new energy vehicles, including the following steps:

[0062] S1. Temperature data is collected through sensors, and the collected temperature data is preprocessed. The control part includes the vehicle controller (VCU), power battery and management system (BMS), electric compressor, cab air conditioning system, battery thermal management system, refrigerant pressure sensor, refrigerant pipeline, ambient temperature sensor, solar radiation intensity sensor, refrigerant solenoid valve, and cooling fan. The cab air conditioning system and the battery thermal management system are connected in parallel through refrigerant pipeline.

[0063] The cab air conditioning system includes a cab air conditioning control panel, a cab interior temperature sensor, an expansion valve, an evaporator, and a blower. The interior temperature sensor monitors the current room temperature at various locations in the cab in real time. The evaporator, together with the compressor, condenser, and expansion valve, forms a complete cab refrigeration circuit through refrigerant piping.

[0064] The power battery and management system (BMS) includes the power battery and its management system, expansion valve, semiconductor refrigeration heat exchanger (Chiller), water pump, water circuit, water pump inlet and outlet temperature sensors, and expansion tank. The semiconductor refrigeration heat exchanger (Chiller) forms a refrigeration circuit with the compressor, condenser, and expansion valve through refrigerant piping, and at the same time, it uses a semiconductor refrigeration module to achieve precise heat regulation.

[0065] An ambient temperature sensor acquires the current ambient temperature in real time, a solar radiation intensity sensor acquires the solar radiation intensity around the cab in real time, and a refrigerant solenoid valve controls the flow of refrigerant in the pipeline. When the cab cooling request is activated, the compressor speed, water pump operating status, and the on / off status of the first and second refrigerant solenoid valves are adjusted in a timely manner based on the current cab temperature, evaporator temperature, power battery temperature, vehicle ambient temperature, and current solar radiation intensity.

[0066] In this embodiment S1, the temperature data includes the cab temperature, evaporator temperature, vehicle ambient temperature, and ambient solar radiation intensity data.

[0067] In this embodiment S1, temperature data is collected by a sensor, and the collected temperature data is preprocessed. The specific steps of the preprocessing operation are as follows:

[0068] S1.1 Identify and remove outliers using the standard deviation in statistical methods, and fill in missing values ​​using linear interpolation in interpolation methods;

[0069] S1.2, Based on the temperature data in S1.1, the Butterworth filter technique in the low-pass filtering technology is used to reduce the impact of noise on temperature measurement;

[0070] S1.3. The temperature data after filtering is normalized using Z-Score to convert data of different scales to the same range.

[0071] S2. Based on the preprocessed temperature data, set the evaporator temperature protection threshold and construct a multivariable adaptive control function; wherein, when the evaporator temperature is close to the evaporator temperature protection threshold, the system will adjust the refrigerant flow direction by opening the BMS-side refrigerant solenoid valve to reduce the refrigerant flow into the evaporator and prevent the evaporator from becoming too cold.

[0072] In this embodiment S2, the specific steps for constructing a multivariable adaptive control function based on the preprocessed temperature data are as follows:

[0073] S2.1. Define the state vector x based on the cab temperature and evaporator temperature, and define the disturbance input vector d based on the vehicle's external ambient temperature and external solar radiation intensity. Then, the mathematical expression involved in defining the state vector x is:

[0074]

[0075] In the formula, T cabin Indicates the temperature inside the cab; T evaporator Indicates the evaporator temperature;

[0076] The mathematical expression for the perturbation input vector d is then defined as follows:

[0077]

[0078] In the formula, T ambient Indicates the ambient temperature; S solar Indicates the intensity of external solar radiation;

[0079] S2.2. Based on the vehicle's heating power, the control input vector u is defined. The mathematical expression for the control input vector u is:

[0080] u = Q heater ;

[0081] In the formula, Q heater This indicates the heating capacity of new energy vehicles;

[0082] S2.3 Construct a multivariable adaptive control function f(x,u,d) based on the state vector x obtained in S2.1, the disturbance input vector d, and the control input vector u obtained in S2.2.

[0083] In this embodiment S2.3, the rate of change of the state vector x is derived based on the state vector x, the disturbance input vector d, and the control input vector u, thereby constructing a multivariable adaptive control function f(x,u,d). The specific method for deriving the rate of change of the state vector x based on the state vector x, the disturbance input vector d, and the control input vector u is as follows:

[0084] The change in cab temperature is obtained from the heat balance equation, and the expression for the change in cab temperature is:

[0085]

[0086] Among them, Q in =α in (T evaporator -T cabin );Q out =α out (T cabin -T ambient );Qsolar =β solar S solar ;

[0087] Q in Q out and Q solar Substituting the mathematical expression for the temperature change in the cab into the heat balance equation, the expression for the temperature change in the cab is:

[0088]

[0089] Where, Q represents the change in temperature inside the driver's cab per unit time; in α represents the heat entering the driver's cab. in This indicates the efficiency of heat transfer from the evaporator to the interior of the driver's cab; Q out α represents the heat emitted from the driver's cab to the outside environment. out This indicates the efficiency of heat loss from the cab to the outside; Q solar β represents the heating effect of solar radiation on the driver's cab. solar This indicates the material's ability to absorb solar energy;

[0090] The change in evaporator temperature is obtained from the energy balance equation, and the expression for the change in evaporator temperature is:

[0091]

[0092] in,

[0093] Will and Substituting the mathematical expression into the energy balance equation, the expression for the temperature change in the evaporator is:

[0094]

[0095] Where, α represents the amount of heat absorbed by the evaporator. evap This indicates the efficiency of heat transfer from the cab to the evaporator; α represents the rate at which heat is lost from the evaporator to the external environment. loss This represents the efficiency of heat loss from the evaporator to the external environment; m represents the mass of the evaporator material; c p This indicates the specific heat capacity of a substance under constant pressure.

[0096] Substituting the derived rate of change of the state vector x into the multivariable adaptive control function f(x,u,d), the expression for f(x,u,d) is:

[0097]

[0098] in,

[0099]

[0100]

[0101] In the formula, f1 represents the rate of change of cab temperature over time; f2 represents the rate of change of evaporator temperature over time.

[0102] S3. Based on the constructed multivariable adaptive control function, the compressor speed, the state of the refrigerant solenoid valve, and the closing time of the refrigerant solenoid valve are dynamically adjusted through the model predictive control algorithm.

[0103] In this embodiment S3, the specific steps for dynamically adjusting the compressor speed, the state of the refrigerant solenoid valve, and the closing time of the refrigerant solenoid valve based on the constructed multivariable adaptive control function and the model predictive control algorithm are as follows:

[0104] S3.1. Extending the compressor speed and refrigerant solenoid valve state into the control input vector u, the mathematical expression for the control input vector is:

[0105]

[0106] In the formula, u compressor Indicates compressor speed; u valve Indicates the status of the refrigerant solenoid valve; t off Indicates the closing time of the refrigerant solenoid valve;

[0107] S3.2. Define the objective function J based on the control input vector u. The objective function J is then expressed as:

[0108]

[0109] In the formula, N represents the prediction time domain length, i.e., how many time steps are predicted for the future; T cabin (k+i) represents the cab temperature at time k+i; T target Indicates the target temperature of the cab; Q heater (k+i) represents the heating power at time k+i; u compressor (k+i) represents the compressor speed at time k+i; u valve (k+i) represents the state of the refrigerant solenoid valve at time k+i; t off,refw1 indicates the desired refrigerant solenoid valve closing time; w2 indicates the importance of the rate of change in cab temperature error; w3 indicates the importance of the rate of change in heating power consumption; w4 indicates the importance of the rate of change in compressor speed; w5 indicates the importance of the rate of change in refrigerant solenoid valve status; w5 indicates the importance of the deviation of the refrigerant solenoid valve closing time from the reference value.

[0110] S3.3. The control input sequence u(k) at each sampling time k is obtained by solving for the minimum value of the objective function J, and the objective function J is constrained by the upper and lower limits of the control input vector u according to the state update equation and the upper and lower limits of the objective function J. The expression for solving for the minimum value of the objective function J is:

[0111]

[0112] In the formula, u(k) represents the control input sequence at time k;

[0113] S3.4. Based on the first control action u(k), dynamically adjust the compressor speed, the state of the refrigerant solenoid valve, and the closing time of the refrigerant solenoid valve, and recalculate the optimal control sequence at the next sampling time.

[0114] In this embodiment S3.2, the weight coefficients of each item in the objective function J are adjusted using an adaptive weight adjustment technique, wherein the adjustment method for each weight coefficient is as follows:

[0115] S3.2.1 Calculate the deviation and rate of change of the weight coefficients of the objective function J based on the performance index deviations. Then, the deviation and rate of change of each performance index are:

[0116] Cab temperature deviation:

[0117] e1(k)=T cabin (k)-T target ;

[0118] In the formula, e1(k) represents the cab temperature deviation at time k;

[0119] The rate of change of cab temperature is:

[0120] Δe1(k)=e1(k)-e1(k-1);

[0121] In the formula, Δe1(k) represents the rate of change of the cab temperature at time k;

[0122] The heating power deviation is:

[0123] e2(k)=Q heater (k)-Q heater,ref ;

[0124] In the formula, e2(k) represents the heating power deviation at time k;

[0125] The rate of change of heating power is:

[0126] Δe2(k)=e2(k)-e2(k-1);

[0127] In the formula, Δe2(k) represents the rate of change of heating power at time k;

[0128] The compressor speed deviation is:

[0129] e3(k)=u compressor (k)-u compressor,ref ;

[0130] In the formula, e3(k) represents the compressor speed deviation at time k;

[0131] The compressor speed change rate is:

[0132] Δe3(k)=e3(k)-e3(k-1);

[0133] In the formula, Δe3(k) represents the rate of change of compressor speed at time k;

[0134] The refrigerant solenoid valve status deviation is:

[0135] e4(k)=u valve (k)-u valve,ref ;

[0136] In the formula, e4(k) represents the refrigerant solenoid valve state deviation at time k;

[0137] The refrigerant solenoid valve state change rate is:

[0138] Δe4(k) = e4(k) - e4(k-1);

[0139] In the formula, Δe4(k) represents the rate of change of the refrigerant solenoid valve state at time k;

[0140] The refrigerant solenoid valve closing time deviation is:

[0141] e5(k)=t off (k)-t off,ref ;

[0142] In the formula, e5(k) represents the refrigerant solenoid valve closing time deviation at time k;

[0143] The rate of change of the closing time of the refrigerant solenoid valve is:

[0144] Δe5(k) = e5(k) - e5(k-1);

[0145] In the formula, Δe5(k) represents the rate of change of the closing time of the refrigerant solenoid valve at time k;

[0146] S3.2.2. Based on the deviation and rate of change obtained in S3.2.1, the weight coefficients are dynamically adjusted using the PID adjustment method, and normalization is performed after each adjustment. The expression for dynamically adjusting the weight coefficients using the PID adjustment method is then:

[0147]

[0148] In the formula, K p1 K represents the proportional gain coefficient for cab temperature. i1 K represents the integral gain coefficient for cab temperature. d1 This represents the differential gain coefficient for cab temperature.

[0149] The expression for dynamically adjusting the heating power weighting coefficient is:

[0150]

[0151] In the formula, K p2 K represents the proportional gain coefficient of heating power. i2 K represents the integral gain coefficient of heating power; d2 This represents the differential gain coefficient of heating power;

[0152] The expression for dynamically adjusting the compressor speed weighting coefficient is:

[0153]

[0154] In the formula, K p3 K represents the compressor speed proportional gain coefficient. i3 K represents the integral gain coefficient of the compressor speed. d3 This represents the differential gain coefficient of the compressor speed;

[0155] The expression for dynamically adjusting the state weighting coefficient of the refrigerant solenoid valve is:

[0156]

[0157] In the formula, K p4 K represents the proportional gain coefficient of the refrigerant solenoid valve. i4 K represents the integral gain coefficient of the refrigerant solenoid valve. d4 This represents the differential gain coefficient representing the state of the refrigerant solenoid valve;

[0158] The expression for the weighting coefficient of the refrigerant solenoid valve closing time is as follows:

[0159]

[0160] In the formula, Kp5 K represents the proportional gain coefficient for the closing time of the refrigerant solenoid valve. i5 K represents the integral gain coefficient of the refrigerant solenoid valve closing time. d5 This represents the differential gain coefficient of the refrigerant solenoid valve closing time;

[0161] S3.2.3. Set the trigger conditions for weight adjustment based on the adjusted weight coefficients to reduce computational overhead;

[0162] S3.2.4 Repeat steps S3.2.1 to S3.2.3 at each sampling time until the system performance meets the design requirements.

[0163] In this embodiment S3.3, based on the objective function J constrained by the state update equation, the expression of the state update equation is:

[0164] The state update equation for adjusting the compressor speed is:

[0165] u compressor (k+1)=a2(T target -T evaporator (k))+b2u compressor (k);

[0166] In the formula, u compressor (k+1) represents the compressor speed at time k+1; a2 is used to convert the temperature difference into a change in compressor speed; b2 determines the degree of influence of the current compressor speed on the speed at the next time step.

[0167] The state update equation for adjusting the refrigerant solenoid valve state is:

[0168] u valve (k+1)=u valve (k)+f(u valve (k));

[0169] In the formula, u valve (k+1) represents the state of the refrigerant solenoid valve at time k+1;

[0170] The state update equation for adjusting the closing time of the refrigerant solenoid valve is:

[0171] t off (k+1)=(1-u valve (k))·(t off (k)+Δt);

[0172] In the formula, t off (k+1) represents the closing time of the refrigerant solenoid valve at time k+1.

[0173] In this embodiment S3.3, based on the objective function J for the upper and lower limit constraints of the control input vector u, the expression for the upper and lower limit constraints of the control input vector u is:

[0174] The heating power constraint is:

[0175]

[0176] Where, Indicates the minimum allowable heating power of the system; Indicates the maximum allowable heating power of the system;

[0177] The compressor speed constraint is:

[0178]

[0179] Where, Indicates the minimum allowable speed of the compressor; Indicates the maximum allowable speed of the compressor;

[0180] The refrigerant solenoid valve state constraints are as follows:

[0181] u valve ∈{0,1};

[0182] In the formula, u valve =0 indicates that the refrigerant solenoid valve is closed; u valve =1 indicates that the refrigerant solenoid valve is open;

[0183] The closing time constraint for the refrigerant solenoid valve is:

[0184]

[0185] Where, Indicates the minimum allowed shutdown time; Indicates the maximum allowed shutdown time.

[0186] Under the condition that the vehicle is powered on normally and all components are working properly, and the vehicle's SOC is higher than 20%, if the PTC in the cab is not working and the refrigerant pressure is not too high or too low, when the cab air conditioning panel requests to start the compressor's cooling, the condenser cooling fan is turned on first, high voltage is supplied to the electric compressor, and a start command is sent to the compressor. Based on the current cab temperature, evaporator temperature, vehicle ambient temperature, and solar radiation intensity, the compressor speed and the refrigerant solenoid valve's on / off status are calculated.

[0187] If the refrigerant pressure in the pipeline remains within the normal range during compressor operation, the compressor will continue to operate at the expected speed. If the current mode is single-cab cooling, the BMS temperature has not yet exceeded the limit.

[0188] If the refrigerant pressure in the pipeline remains within the normal range during compressor operation, the compressor will continue to operate at the desired speed. If the current mode is single-battery cooling, i.e., the cab does not request cooling, and the power battery temperature rises slowly, the power battery will be cooled first through the water circuit. If the power battery temperature rises quickly, a start command will be sent to the compressor in single-BMS cooling mode, and the power battery temperature will be quickly reduced through the semiconductor cooling heat exchanger (Chiller) and the water circuit.

[0189] By comprehensively considering the current temperature of the cab, the current temperature of the evaporator, the current ambient temperature of the vehicle, and the current intensity of solar radiation, the compressor keeps itself at the optimal speed and optimal cooling capacity in real time, ensuring that when the cab is rapidly cooled, excess cooling capacity can be effectively used for the heat dissipation of the power battery.

[0190] Because large-displacement compressors have a minimum speed limit, and the evaporator is not matched with the large-displacement compressor system, even at the lowest speed, the evaporator can easily cool down rapidly to the critical protection temperature.

[0191] Under the above conditions, the temperature of the cab and evaporator is continuously reduced. At this time, combined with the external temperature sensor and the solar radiation intensity sensor, if the compressor is always working when the external temperature is low and the solar radiation is weak, the refrigerant solenoid valves on both sides are kept open to protect the cab evaporator from reaching the critical protection temperature due to the low external environment, thereby avoiding frequent start-stop of the compressor.

[0192] When both the cab and the power battery request cooling simultaneously, the cooling requirements of the cab and the power battery are dynamically adjusted based on the power battery temperature and external environmental conditions. The system also monitors refrigerant pressure to prevent damage. If the power battery temperature is below the target temperature, water cooling is maintained. Feedback from external temperature sensors and solar radiation intensity sensors is used to ensure the entire thermal management system operates as if in single-cab mode, effectively cooling the cab while slowly cooling the power battery. If the power battery temperature is above the target temperature, the compressor operates at its highest speed to ensure cooling on both sides, meeting the cooling requirements of both. Once the cab evaporator temperature exceeds 15 degrees Celsius, the closing time of the refrigerant solenoid valve at the power battery is adjusted based on the power battery temperature, using feedback from external temperature sensors and solar radiation intensity sensors, to lower the cab's interior temperature and meet the cab's cooling needs.

[0193] S4. Based on the status of the refrigerant solenoid valve, the system judges the cumulative refrigerant pressure abnormality under a single power-on condition to realize refrigerant system fault diagnosis and early warning.

[0194] In this embodiment S4, by judging the cumulative refrigerant pressure abnormality under a single power-on condition based on the abnormality detection algorithm, if the frequent start-stop caused by the refrigerant pressure abnormality exceeds 5 times, the compressor cannot be started again under that power-on condition, and the fault data and fault cause during the period are stored in EEPROM; where a single power-on condition refers to the entire operating cycle of the equipment from the power-on to the power-off condition. During this cycle, the equipment will experience normal start-up, operation, and possible shutdown and restart due to various reasons (such as refrigerant pressure abnormality).

[0195] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A thermal management control method for new energy vehicles, characterized in that, Includes the following steps: S1. Collect temperature data through sensors and perform preprocessing operations on the collected temperature data; S2. Set the evaporator temperature protection threshold based on the preprocessed temperature data and construct a multivariable adaptive control function; S3. Based on the constructed multivariable adaptive control function, the compressor speed, the state of the refrigerant solenoid valve, and the closing time of the refrigerant solenoid valve are dynamically adjusted through the model predictive control algorithm. S4. Based on the status of the refrigerant solenoid valve, the system judges the cumulative refrigerant pressure abnormality under a single power-on condition to realize refrigerant system fault diagnosis and early warning.

2. The thermal management control method for new energy vehicles according to claim 1, characterized in that: In S1, the temperature data includes cab temperature, evaporator temperature, vehicle ambient temperature, and ambient solar radiation intensity.

3. The thermal management control method for new energy vehicles according to claim 1, characterized in that: In step S1, temperature data is collected by a sensor, and the collected temperature data is preprocessed. The specific steps of the preprocessing operation are as follows: S1.1 Identify and remove outliers using statistical methods, and fill in missing values ​​using interpolation. S1.2, Based on the temperature data from S1.1, low-pass filtering technology is used to reduce the impact of noise on temperature measurement; S1.

3. The temperature data after filtering is normalized using Z-Score to convert data of different scales to the same range.

4. The thermal management control method for new energy vehicles according to claim 1, characterized in that: In step S2, an evaporator temperature protection threshold is set based on the pre-processed temperature data. When the evaporator temperature approaches the evaporator temperature protection threshold, the system will adjust the refrigerant flow direction by opening the refrigerant solenoid valve on the BMS side to prevent the evaporator from becoming too cold.

5. The thermal management control method for new energy vehicles according to claim 1, characterized in that: In step S2, the specific steps for constructing a multivariable adaptive control function based on the preprocessed temperature data are as follows: S2.1 Define the state vector x based on the cab temperature and evaporator temperature, and define the disturbance input vector d based on the vehicle's external ambient temperature and external solar radiation intensity; S2.2 Define the control input vector u according to the vehicle's heating power; S2.3 Construct a multivariable adaptive control function f(x,u,d) based on the state vector x obtained in S2.1, the disturbance input vector d, and the control input vector u obtained in S2.

2.

6. The thermal management control method for new energy vehicles according to claim 1, characterized in that: In step S3, the specific steps for dynamically adjusting the compressor speed, the state of the refrigerant solenoid valve, and the closing time of the refrigerant solenoid valve based on the constructed multivariable adaptive control function and the model predictive control algorithm are as follows: S3.1, Extend the compressor speed and refrigerant solenoid valve status into the control input vector u; S3.

2. Define the objective function J based on the control input vector u. The objective function J is then expressed as: In the formula, N represents the prediction time domain length; T cabin (k+i) represents the cab temperature at time k+i; T target Indicates the target temperature of the cab; Q heater (k+i) represents the heating power at time k+i; u compressor (k+i) represents the compressor speed at time k+i; u valve (k+i) represents the state of the refrigerant solenoid valve at time k+i; t off,ref w1 indicates the desired refrigerant solenoid valve closing time; w2 indicates the importance of the rate of change in cab temperature error; w3 indicates the importance of the rate of change in heating power consumption; w4 indicates the importance of the rate of change in compressor speed; w5 indicates the importance of the rate of change in refrigerant solenoid valve status; w5 indicates the importance of the deviation of the refrigerant solenoid valve closing time from the reference value. S3.

3. The control input sequence u(k) at each sampling time k is obtained by solving for the minimum value of the objective function J, and the objective function J is constrained according to the state update equation and the upper and lower limits of the control input vector u. S3.

4. Based on the first control action u(k), dynamically adjust the compressor speed, the state of the refrigerant solenoid valve, and the closing time of the refrigerant solenoid valve, and recalculate the optimal control sequence at the next sampling time.

7. The thermal management control method for new energy vehicles according to claim 6, characterized in that: In step S3.2, the weight coefficients of each term in the objective function J are adjusted using an adaptive weight adjustment technique, wherein the adjustment method for each weight coefficient is as follows: S3.2.1 Calculate the deviation and rate of change of the weight coefficients of the objective function J based on the deviation of the performance index; S3.2.2 Based on the deviation and rate of change obtained in S3.2.1, the weight coefficients are dynamically adjusted using the PID adjustment method, and normalization is performed after each adjustment. S3.2.

3. Set the trigger conditions for weight adjustment based on the adjusted weight coefficients to reduce computational overhead; S3.2.4 Repeat steps S3.2.1 to S3.2.3 at each sampling time until the system performance meets the design requirements.

8. The thermal management control method for new energy vehicles according to claim 6, characterized in that: In step S3.3, based on the objective function J constrained by the state update equation, the expression of the state update equation is: The state update equation for adjusting the compressor speed is: u compressor (k+1)=a2(T target -T evaporator (k))+b2u compressor (k); In the formula, u compressor (k+1) represents the compressor speed at time k+1; a2 is used to convert the temperature difference into a change in compressor speed; b2 determines the degree of influence of the current compressor speed on the speed at the next time step. The state update equation for adjusting the refrigerant solenoid valve state is: in valve (k+1)=u valve (k)+f(u valve (k)); In the formula, u valve (k+1) represents the state of the refrigerant solenoid valve at time k+1; The state update equation for adjusting the closing time of the refrigerant solenoid valve is: t off (k+1)=(1-u valve (k))·(t off (k)+Δt); In the formula, t off (k+1) represents the closing time of the refrigerant solenoid valve at time k+1.

9. The thermal management control method for new energy vehicles according to claim 6, characterized in that: In step S3.3, based on the objective function J for the upper and lower limit constraints of the control input vector u, the expression for the upper and lower limit constraints of the control input vector u is: The heating power constraint is: Where, Indicates the minimum allowable heating power of the system; Indicates the maximum allowable heating power of the system; The compressor speed constraint is: Where, Indicates the minimum allowable speed of the compressor; Indicates the maximum allowable speed of the compressor; The refrigerant solenoid valve state constraints are as follows: u valve ∈{0,1}； In the formula, u valve =0 indicates that the refrigerant solenoid valve is closed; u valve =1 indicates that the refrigerant solenoid valve is open; The closing time constraint for the refrigerant solenoid valve is: Where, Indicates the minimum allowed shutdown time; Indicates the maximum allowed shutdown time.

10. The thermal management control method for new energy vehicles according to claim 1, characterized in that: In S4, based on the state of the refrigerant solenoid valve, the cumulative refrigerant pressure abnormality under a single power-on condition is judged to realize the refrigerant system fault diagnosis and early warning. If the frequent start-stop caused by the refrigerant pressure abnormality exceeds 5 times, the compressor cannot be started again under that power-on condition.

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